Running a Model Locally Makes Your Team the Vendor
Running a model locally shifts production work to your team. Evaluate capacity, reliability, security, and engineering time before replacing an external API.
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Running a model locally shifts production work to your team. Evaluate capacity, reliability, security, and engineering time before replacing an external API.
Model selection, caching, context, batch processing, and bounded workflows shape AI costs. Measure the full cost of a successful resolution.
When Claude was banned from government systems, some teams swapped providers in a day. Others faced months of refactoring. The difference? Building every integration for replaceability. Here's why this principle applies to every dependency you have.
Step-by-step tutorial for quantizing Meta's Llama 3 8B Instruct model to INT8 weights and activations (W8A8) using LLMCompressor, then serving it with vLLM for production inference with KV cache and continuous batching.
Explore Meta's groundbreaking Code World Models research that teaches AI to understand code semantics and execution behavior, not just syntax. Learn how this 32B parameter model simulates program execution and what it means for bug-free software development.